European Radiology

, Volume 27, Issue 8, pp 3257–3265 | Cite as

Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases

  • Eui Jin Hwang
  • Jin Mo Goo
  • Jihye Kim
  • Sang Joon Park
  • Soyeon Ahn
  • Chang Min Park
  • Yeong-Gil Shin
Chest
First Online:
Received:
Revised:
Accepted:

Abstract

Objectives

To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth.

Materials and methods

For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE).

Results

SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004)

Conclusion

Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold.

Key Points

• The 95 % percentile APE of a particular nodule can be predicted.

• Estimated 95 % percentile APE can be utilized as an individualized threshold.

• More sensitive diagnosis of metastasis can be made with an individualized threshold.

• Tailored nodule management can be provided during nodule growth follow-up.

Keywords

Lung nodule Measurement error Volumetry Growth Modeling 

Abbreviations

AP

Attachment proportion

APE

Absolute percentage error

CT

Computed tomography

HU

Hounsfield unit

RECIST

Response Evaluation Criteria in Solid Tumours

SVP

Surface voxel proportion

Vm

Mean volume of segmented nodule

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Notes

Acknowledgements

The scientific guarantor of this publication is Jin Mo Goo. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. This study was supported by grant no. 34-2013-0020 from the SK Telecom Research Fund. One of the authors (Soyeon Ahn) has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. No study subjects or cohorts have been previously reported in the literature. Methodology: prospective, diagnostic or prognostic study, performed at one institution.

References

  1. 1.
    Hasegawa M, Sone S, Takashima S et al (2000) Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 73:1252–1259CrossRefPubMedGoogle Scholar
  2. 2.
    Jaffe CC (2006) Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol 24:3245–3251CrossRefPubMedGoogle Scholar
  3. 3.
    Therasse P, Arbuck SG, Eisenhauer EA et al (2000) New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 92:205–216CrossRefGoogle Scholar
  4. 4.
    Oxnard GR, Zhao B, Sima CS et al (2011) Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes. J Clin Oncol 29:3114–3119CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Revel MP, Lefort C, Bissery A et al (2004) Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 231:459–466CrossRefPubMedGoogle Scholar
  6. 6.
    Buckler AJ, Schwartz LH, Petrick N et al (2010) Data sets for the qualification of volumetric CT as a quantitative imaging biomarker in lung cancer. Opt Express 18:15267–15282CrossRefPubMedGoogle Scholar
  7. 7.
    Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI (2000) Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 217:251–256CrossRefPubMedGoogle Scholar
  8. 8.
    Zhao B, Schwartz LH, Moskowitz CS, Ginsberg MS, Rizvi NA, Kris MG (2006) Lung cancer: computerized quantification of tumor response: initial results. Radiology 241:892–898CrossRefPubMedGoogle Scholar
  9. 9.
    Callister MEJ, Baldwin DR, Akram A et al (2015) British Thoracic Society uidelines for the investiation and management of pulmonary nodules. Thorax 70(2):ii1–ii54CrossRefPubMedGoogle Scholar
  10. 10.
    Goodman LR, Gulsun M, Washington L, Nagy PG, Piacsek KL (2006) Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR Am J Roentgenol 186:989–994CrossRefPubMedGoogle Scholar
  11. 11.
    Wormanns D, Kohl G, Klotz E et al (2004) Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 14:86–92CrossRefPubMedGoogle Scholar
  12. 12.
    Zhao B, James LP, Moskowitz CS et al (2009) Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 252:263–272CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Gietema HA, Schaefer-Prokop CM, Mali WP, Groenewegen G, Prokop M (2007) Pulmonary nodules: Interscan variability of semiautomated volume measurements with multisection CT: influence of inspiration level, nodule size, and segmentation performance. Radiology 245:888–894CrossRefPubMedGoogle Scholar
  14. 14.
    Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KTO (2005) Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy. Radiology 235:850–856CrossRefPubMedGoogle Scholar
  15. 15.
    Ko JP, Rusinek H, Jacobs EL et al (2003) Small pulmonary nodules: volume measurement at chest CT: phantom study. Radiology 228:864–870CrossRefPubMedGoogle Scholar
  16. 16.
    Marten K, Funke M, Engelke C (2004) Flat panel detector-based volumetric CT: prototype evaluation with volumetry of small artificial nodules in a pulmonary phantom. J Thorac Imaging 19:156–163CrossRefPubMedGoogle Scholar
  17. 17.
    Petrou M, Quint LE, Nan B, Baker LH (2007) Pulmonary nodule volumetric measurement variability as a function of CT slice thickness and nodule morphology. AJR Am J Roentgenol 188:306–312CrossRefPubMedGoogle Scholar
  18. 18.
    van Klaveren RJ, Oudkerk M, Prokop M et al (2009) Management of lung nodules detected by volume CT scanning. N Eng J Med 361:2221–2229CrossRefGoogle Scholar
  19. 19.
    Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 22:1259–1274CrossRefPubMedGoogle Scholar
  20. 20.
    de Hoop B, Gietema H, van Ginneken B, Zanen P, Groenewegen G, Prokop M (2009) A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. Eur Radiol 19:800–808CrossRefPubMedGoogle Scholar
  21. 21.
    Wadell H (1935) Volume, shape, and roundness of quartz particles. J Geol 43:250–280CrossRefGoogle Scholar
  22. 22.
    Koenker R, Bassett GW Jr (1978) Regression Quantiles Econometrica 46:33–50CrossRefGoogle Scholar
  23. 23.
    Cade BS, Noon BR (2003) A gentle introduction to quantile regression for ecologists. Front Ecol Environ 1:412–420CrossRefGoogle Scholar
  24. 24.
    Marrie RA, Dawson NV, Garland A (2009) Quantile regression and restricted cubic splines are useful for exploring relationships between continuous variables. J Clin Epidemiol 62(511–517), e1Google Scholar
  25. 25.
    Goo JM (2011) A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. Korean J Radiol 12:145–155CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Gavrielides MA, Kinnard LM, Myers KJ, Petrick N (2009) Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 251:26–37CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ (2006) On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 25:435–450CrossRefPubMedGoogle Scholar
  28. 28.
    Das M, Ley-Zaporozhan J, Gietema HA et al (2007) Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. Eur Radiol 17:1979–1984CrossRefPubMedGoogle Scholar
  29. 29.
    Kuhnigk JM, Dicken V, Zidowitz S et al (2005) Informatics in radiology (infoRAD): new tools for computer assistance in thoracic CT. Part 1. Functional analysis of lungs, lung lobes, and bronchopulmonary segments. Radiographics 25:525–536CrossRefPubMedGoogle Scholar
  30. 30.
    Kostis WJ, Yankelevitz DF, Reeves AP, Fluture SC, Henschke CI (2004) Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 231:446–452CrossRefPubMedGoogle Scholar
  31. 31.
    Petkovska I, Brown MS, Goldin JG et al (2007) The effect of lung volume on nodule size on CT. Acad Radiol 14:476–485CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Weiss E, Wijesooriya K, Dill SV, Keall PJ (2007) Tumor and normal tissue motion in the thorax during respiration: Analysis of volumetric and positional variations using 4D CT. Int J Radiat Oncol, Biol, Phys 67:296–307CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  1. 1.Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation MedicineSeoul National University Medical ResearchSeoulKorea
  2. 2.Deparment of RadiologyArmed Forces Seoul HospitalSeoulKorea
  3. 3.Cancer Research InstituteSeoul National UniversitySeoulKorea
  4. 4.School of Computer Science and EngineeringSeoul National UniversitySeoulKorea
  5. 5.Medical Research Collaborating CenterSeoul National University Bundang HospitalSeongnam-siKorea

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